Embodiments of the invention described in this specification relate generally to human-like artificial intelligence, and more particularly, to human-like artificial intelligence of relational robotic controller (RRC)-controlled Humanoid robotic systems.
The design of “thinking computers” has been a goal of the discipline of Artificial Intelligence (AI) since the advent of digital computers. In 1950, Alan Turing, arguably, the founder of AI, posed the question “when is a machine thinking′?” His approach to an answer was in terms of the behavior of the machine (Turing, A. M. 1950; “Computing machinery and Intelligence” Mind, 59 433-60). He devised an I.Q. ‘Turing test’ based on the conversational behavior of the machine; and deemed any machine that passed the I.Q.-test to be a thinking machine.
Following Alan Turing, this disclosure describes a building path for a machine that can reach human-like levels of verbal Artificial Intelligence (AI), defined in terms of the verbal behavior of the machine. But instead of programming the computer with AI, we first program a ‘robotic self’ into the system, that identifies the robotic system, and then program, experientially, all the AI that the robot gains with respect to, or into the robotic self coordinate frame of the system. So that it is the robotic self that develops a high IQ-level of intelligence, NOT the objective-mechanical digital computer system.
We have thereby designed a system, called a Relational Robotic Controller (RRC)-system that has a subjective identity and AI-knowledge associated with that identity. It is the ‘robotic self,’ programmed into the computer that has verbal intelligence, not the objective-mechanical digital computer.
A Note about Human-Like Levels of AI
Human-like levels of AI have never before been programmed into computer systems. For that reason, embodiments of the invention described in this disclosure differentiate between objective data and subjective data (data programmed with respect to a ‘robotic self’ coordinate frame of the system). Objective data represents the data programmed into most of the present day digital computers and computing devices. By use of symbolic logic algorithms these computing devices may exhibit forms of artificial intelligence. However, this specification labels all such intelligence as machine-like intelligence, rather than human-like intelligence. Machine-like intelligence may, therefore, refer to the objective knowledge programmed into all modern day computing devices. In contrast, human-like intelligence refers to the data programmed into the computing system with respect to the robotic self-coordinate frame of the system.
All programmable digital computers do not have a “self identity” as a human does, that could absorb and convert all data into subjective knowledge, knowledge absorbed relative to the “self” of the machine. Therefore, the ordinary computers do not have human-like intelligence, they have machine-like intelligence.
Machine-like intelligence may refer to the objective knowledge programmed into all modern day computing devices. Human-like intelligence is obtained relative to the “self” of the machine. Human-like intelligence is called subjective knowledge.
The following are six requirements of human intelligence that are fundamental to any quantitative measure of intelligence. When those six requirements are imposed on a robotic computer system, the system may achieve human-like levels of AI. Those six requirements also form the basis for a quantitative definition of human-like AI (see lexicography section).
Requirement #1. The Robotic Controller Must Relate, Correlate, Prioritize and Remember Sensory Input Data.
It has been observed that human intelligence in the human brain is generally achieved by relating, correlating, prioritizing and remembering input patterns that are observed by the human sensory system (consisting of the tactile, visual, auditory, olfactory and gustatory sensors). Therefore relating, correlating, prioritizing and remembering must be the essential analytic tool of a robotic controller. The RRC, (a proprietary robotic controller of MCon Inc.), was specifically designed to emulate the operation of the human brain. It also was designed to operate with a ‘self’ circuit that is the central hub of intelligence for the whole robotic system.
Requirement #2. The Robotic System Must have Proprioceptive Knowledge.
Humans have a self-location and identification coordinate frame that is trained from infancy to give the human brain a proprioceptive self-knowledge capability. Even a baby, with a self-knowledge capability, instinctively knows the location of every surface point on its body, the location of its flailing limbs, and by extension, the location of every coordinate frame point in the near space defined by its flailing limbs. The fundamental design characteristic of any human-like intelligent system is a centralized hub of intelligence that is the centralized “self location and identification” coordinate frame of the system. The RRC-Humanoid Robot is designed to give the robot a form of proprioceptive knowledge, similar to human proprioceptive intelligence. In the RRC-Robot, the self-knowledge capability is the basis for all knowledge.
Requirement #3. Contextual ‘Self-Knowledge’ of Other Sensory Data Must be Achieved by Relating/Correlating with the Self-Location and Identification Coordinate Frame of the System.
In order to achieve contextual ‘self-knowledge’ of the visual data, auditory data, olfactory data, gustatory data, and vestibular data, all the data obtained from those human-like sensors must be related and correlated with the self-knowledge, self-location and identification coordinate frame. The RRC is ideally suited to relate and correlate the visual, auditory, olfactory, and gustatory data with the self-location and identification coordinate frame that serves as the central hub of intelligence of an RRC-robotic system.
Requirement #4. Human Intelligence is Gained Only from the Human-Like Sensors.
In this disclosure we consider the external sensors: Tactile, visual, auditory, olfactory, gustatory, and vestibular sensors. These sensors provide for the sensations associated with human ‘feeling,’ ‘ seeing,’ ‘hearing,’ ‘smelling,’ ‘tasting,’ and ‘balancing,’ respectively.
The recording monitors of the RRC-Humanoid Robot are mechano-electric sensors that emulate the external sensors of humans. The 6-robotic sensors should be human-like sensors designed to gain the same information as is gained by the human sensors. These sensors provide for behavioral/experiential intelligence associated with ‘experiential feeling,’ ‘experiential seeing,’, ‘experiential hearing,’ ‘experiential smelling,’ ‘experiential tasting,’ and ‘experiential balancing.’ See the disclaimer at the end of the Detailed Description of the Invention Section to clarify that the inventors claim that the robot behaves as if it ‘feels,’ ‘sees,’ ‘hears,’ ‘smells,’ or ‘tastes,’ the input data.
Requirement #5. Human Intelligence is Experiential Intelligence.
Humans learn from, and remember their experiences throughout their lifetime. A behaviorally programmed human-like system has a memory system that remembers the experiences of the robot and emulates the experiential intelligence of a human. The RRC robot has a memory system that may be behaviorally programmed to remember all its experiences.
Requirement #6. Human-Like Intelligence is Gained Only by a Mechanically Human-Like Robotic System.
The mechanical robotic body and associated sensors must simulate the human body and the human sensors. The robotic body must be bipedal, standing and walking upright with two arms, hands and five fingers per hand free to manipulate objects in the environment. The six (6) robotic sensors should be human-like sensors designed to gain the same information as is gained by the human sensors. The mechanical robotic body of the RRC-Humanoid Robot emulates the static and dynamic characteristics of the human body.
Those six requirements must be fulfilled by any robotic computer/controller in order to have a human-like AI capability. Those six requirements form the basis for the robotic definition of human-like intelligence (see lexicography section).